APPLICATION OF MACHINE LEARNING IN PROCESS OPTIMIZATION OF TURNING AND MILLING OPERATIONS
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Manufacturing industries continuously strive to improve productivity, surface quality, and tool life while minimizing cost. Traditional optimization techniques such as trial-and-error and statistical methods often fall short in handling complex nonlinear relationships among machining parameters. This paper presents the application of Machine Learning (ML) techniques for optimizing process parameters in turning and milling operations. Models such as Linear Regression, Decision Trees, and Artificial Neural Networks (ANN) are developed to predict output responses like surface roughness and material removal rate (MRR). Experimental data is used to train and validate the models. The results demonstrate that ML-based optimization significantly improves prediction accuracy and process efficiency compared to conventional methods.
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lamkane, A. & gaikwad, R. (2026). Application of Machine Learning in Process Optimization of Turning and Milling Operations. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.310
lamkane, Anup, and Rushikesh gaikwad. "Application of Machine Learning in Process Optimization of Turning and Milling Operations." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.310.
lamkane, Anup, and Rushikesh gaikwad. "Application of Machine Learning in Process Optimization of Turning and Milling Operations." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.310.
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